Hotel Recommendation System

In the era of digital transformation, travelers increasingly depend on online platforms to identify accommodations that align with their unique travel intentions and preferences. The sheer volume and diversity of hotel options available online often make this selection process challenging and time-consuming. This paper presents a hotel recommendation system developed using Flask, a lightweight Python web framework, and enhanced by natural language processing (NLP) techniques to address this challenge. The proposed system enables users to input a brief, natural-language description of their travel purpose—such as business trips, honeymoons, or family vacations—alongside their preferred destination. Utilizing basic NLP methods, including tokenization, stopword removal, and lemmatization, the system processes user input and matches it against hotel tags and review metadata through a similarity-matching algorithm. Recommendations are then ranked by relevance and review scores, ensuring that users receive personalized, high-quality hotel suggestions tailored to their needs. The application features distinct roles for administrators and standard users, supporting robust management and a user-friendly experience. Administrators can manage users, FAQs, and tourist information, while travelers benefit from streamlined hotel recommendations and access to relevant travel resources. Experimental evaluation demonstrates the system’s effectiveness in improving user satisfaction and efficiency in hotel selection, highlighting its practical value for both travelers and the hospitality industry.